Many works for one-shot VC are based on speech representation disentanglement (SRD), which aim to separate speaker information from spoken content as much as possible.
The AutoVC [1]} comes up with the idea to combine the advantages of generative adversarial network (GAN) [2]} and conditional variational auto-encoder (CVAE) [3]} since GAN can obtain a good result while CVAE is easy to train.
The IVC system and the SEVC system [4]} represent the speaker
identity as i-vectors and speaker embedding to perform one-shot VC.
The unsupervised end-to-end automatic speech recognition and text-to-speech (ASR-TTS) autoencoder framework [5]} use multilabel-binary vectors to represent the content of speech to dientangle content from speaker.
An F0-conditioned voice conversion system [6]} disentangles prosodic features by tuning the information-constraining bottleneck of an autoencoder.
Due to the ability of SRD to disentangle latent space, there are many other methods [7]}, [1]}, [2]}, [10]}, [11]}, [12]} based on SRD for VC.
